Automated detection of inflammation in foot thermographic images of patients with diabetes acquired in an uncontrolled clinical environment

Aims Quantitative methods for detecting diabetic foot inflammation remain limited, though localized skin-temperature elevation is a promising early indicator. Because background variability can hinder AI-based foot identification, we evaluated AI-driven thermal imaging for diabetic foot inflammation under uncontrolled clinical backgrounds and assessed whether curtain-based background control and grayscale conversion improved detection. Methods Cross-sectional study design was conducted in Makassar, Indonesia, 100 plantar thermal images from 50 diabetic patients were acquired with and without a curtain, preprocessed (sensitivity, 1.5; emissivity, 0.98), and converted to grayscale. Expert-identified inflammatory hotspots were annotated and used to train a YOLO11n model with 5-fold cross-validation, evaluated using accuracy, precision, sensitivity, specificity, ROC-AUC, IoU, and case-error analysis. Results With a curtain, color-map images achieved sensitivity of 0.773, versus 0.455 without one. Grayscale images performed consistently across conditions, with best results in the mixed dataset (accuracy, 0.980; precision, 0.976; sensitivity, 0.932; specificity, 0.994; AUC 0.966 vs. 0.886 for color-map). Correctly detected cases showed IoU > 0.9. Low thermal contrast and small region of interest were the most frequent failure categories. Conclusions AI-driven thermal imaging detects diabetic foot inflammation under uncontrolled conditions. A curtain improved color-map detection, but grayscale imaging provided robust, curtain-independent performance, supporting its use for clinical screening.